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Population-Based Survey Experiments
Abstract
Population-based survey experiments have become an invaluable tool for social scientists struggling to generalize laboratory-based results, and for survey researchers besieged by uncertainties about causality. Thanks to technological advances in recent years, experiments can now be administered to random samples of the population to which a theory applies. Yet until now, there was no self-contained resource for social scientists seeking a concise and accessible overview of this methodology, its strengths and weaknesses, and the unique challenges it poses for implementation and analysis. Drawing on examples from across the social sciences, this book covers everything you need to know to plan, implement, and analyze the results of population-based survey experiments. But it is more than just a “how to” manual. This book challenges conventional wisdom about internal and external validity, showing why strong causal claims need not come at the expense of external validity, and how it is now possible to execute experiments remotely using large-scale population samples. Designed for social scientists across the disciplines, the book provides the first complete introduction to this methodology and features a wealth of examples and practical advice.
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“Following best practices in vignette design, realistic and high-stakes settings help ensure manipulations are both plausible and psychologically engaging (Mutz 2011).”
From Holding AI Accountable Like Herding Cats: The Contingent Impact on the Legitimacy of Algorithmic Bureaucracy (2026) · cites this paper as Mutz 2011
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Nix, J., Pickett, J. T., & Mitchell, R. J. (2019) · Journal of Experimental Criminology · doi:10.1007/s11292-019-09363-4 · Cited by 41
Hillo, J., Vento, I., & Erkkilä, T. (2026) · Public Administration · doi:10.1111/padm.70028 · Cited by 22
Liu, R., & Han, Y. (2026) · Public Administration · doi:10.1111/padm.70043 · Cited by 2
Metadata
- Authors
- Mutz, D. C.
- Corresponding author
- Mutz, Diana C.
- ORCID
- Mutz, Diana C.
- Venue
- Princeton University Press eBooks
- Publisher
- Princeton University Press
- Published
- 2011-07-25
- OpenAlex
- W4230818916
- Cited by
- 104
- References
- 0
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 25 · 2023: 11 · 2022: 5 · 2021: 4 · 2020: 9 · 2019: 8 · 2018: 6 · 2017: 7 · 2016: 5 · 2015: 2 · 2014: 5 · 2012: 1
- FWCI
- 2.11
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Princeton University Press eBooks (publishedVersion)
- Project Muse (Johns Hopkins University) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- randomized experiments, causal inference, generalizability, internal validity, remote experimentation, experimental design
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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